Detection and Evaluation of Spatio-Temporal Spike Patterns in Massively Parallel Spike Train Data with SPADE

CADENCE - Neuroinformatics Tool for Supervised Calcium Events Detection

CADENCE is an open Python 3-written neuroinformatics tool with Qt6 graphic user interface for supervised calcium events detection. In neuronal ensembles recording during calcium imaging experiments, the output of instruments such as Celena X, Zeiss LSM 5 Live confocal microscope and Miniscope is a movie showing flashing cells somata. There are few pipelines to convert video to relative fluorescence ΔF/F, from simplest ImageJ plugins to sophisticated tools like MiniAn (Dong et al. in Elife 11, https://doi.org/10.7554/eLife.70661 , 2022). Minian, an open-source miniscope analysis pipeline. Elife, 11.). While in some areas of study relative fluorescence ΔF/F may be the desired result in itself, researchers of neuronal ensembles are typically interested in a more detailed analysis of calcium events as indirect proxy of neuronal electrical activity. For such analyses, researchers need a tool to infer calcium events from the continuous ΔF/F curve in order to create a raster representation of calcium events for later use in analysis software, such as Elephant (Denker, M., Yegenoglu, A., & Grün, S. (2018). Collaborative HPC-enabled workflows on the HBP Collaboratory using the Elephant framework. Neuroinformatics, 19.). Here we present such an open tool with supervised calcium events detection.

Propofol modulates neural dynamics of thalamo-cortical system associated with anesthetic levels in rats

The thalamocortical system plays an important role in consciousness. How anesthesia modulates the thalamocortical interactions is not completely known.  We simultaneously recorded local field potentials(LFPs) in thalamic reticular nucleus(TRN) and ventroposteromedial thalamic nucleus(VPM), and electrocorticographic(ECoG) activities in frontal and occipital cortices in freely moving rats (n = 11). We analyzed the changes in thalamic and cortical local spectral power and connectivities, which were measured with phase-amplitude coupling (PAC), coherence and multivariate Granger causality, at the states of baseline, intravenous infusion of propofol 20, 40, 80 mg/kg/h and after recovery of righting reflex. We found that propofol-induced burst-suppression results in a synchronous decrease of spectral power in thalamus and cortex (p < 0.001 for all frequency bands). The cross-frequency PAC increased by propofol, characterized by gradually stronger 'trough-max' pattern in TRN and stronger 'peak-max' pattern in cortex. The cross-region PAC increased in the phase of TRN modulating the amplitude of cortex. The functional connectivity (FC) between TRN and cortex for α/β bands also significantly increased (p < 0.040), with increased directional connectivity from TRN to cortex under propofol anesthesia. In contrast, the corticocortical FC significantly decreased (p < 0.047), with decreased directional connectivity from frontal cortex to occipital cortex. However, the thalamothalamic functional and directional connectivities remained largely unchanged by propofol anesthesia.  The spectral powers and connectivities are differentially modulated with the changes of propofol doses, suggesting the changes in neural dynamics in thalamocortical system could be used for distinguishing different vigilance levels caused by propofol.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-022-09912-0.

Sequence learning, prediction, and replay in networks of spiking neurons

Sequence learning, prediction and replay have been proposed to constitute the universal computations performed by the neocortex. The Hierarchical Temporal Memory (HTM) algorithm realizes these forms of computation. It learns sequences in an unsupervised and continuous manner using local learning rules, permits a context specific prediction of future sequence elements, and generates mismatch signals in case the predictions are not met. While the HTM algorithm accounts for a number of biological features such as topographic receptive fields, nonlinear dendritic processing, and sparse connectivity, it is based on abstract discrete-time neuron and synapse dynamics, as well as on plasticity mechanisms that can only partly be related to known biological mechanisms. Here, we devise a continuous-time implementation of the temporal-memory (TM) component of the HTM algorithm, which is based on a recurrent network of spiking neurons with biophysically interpretable variables and parameters. The model learns high-order sequences by means of a structural Hebbian synaptic plasticity mechanism supplemented with a rate-based homeostatic control. In combination with nonlinear dendritic input integration and local inhibitory feedback, this type of plasticity leads to the dynamic self-organization of narrow sequence-specific subnetworks. These subnetworks provide the substrate for a faithful propagation of sparse, synchronous activity, and, thereby, for a robust, context specific prediction of future sequence elements as well as for the autonomous replay of previously learned sequences. By strengthening the link to biology, our implementation facilitates the evaluation of the TM hypothesis based on experimentally accessible quantities. The continuous-time implementation of the TM algorithm permits, in particular, an investigation of the role of sequence timing for sequence learning, prediction and replay. We demonstrate this aspect by studying the effect of the sequence speed on the sequence learning performance and on the speed of autonomous sequence replay.

Essentially all data processed by mammals and many other living organisms is sequential. This holds true for all types of sensory input data as well as motor output activity. Being able to form memories of such sequential data, to predict future sequence elements, and to replay learned sequences is a necessary prerequisite for survival. It has been hypothesized that sequence learning, prediction and replay constitute the fundamental computations performed by the neocortex. The Hierarchical Temporal Memory (HTM) constitutes an abstract powerful algorithm implementing this form of computation and has been proposed to serve as a model of neocortical processing. In this study, we are reformulating this algorithm in terms of known biological ingredients and mechanisms to foster the verifiability of the HTM hypothesis based on electrophysiological and behavioral data. The proposed model learns continuously in an unsupervised manner by biologically plausible, local plasticity mechanisms, and successfully predicts and replays complex sequences. Apart from establishing contact to biology, the study sheds light on the mechanisms determining at what speed we can process sequences and provides an explanation of fast sequence replay observed in the hippocampus and in the neocortex.

Acceleration of the SPADE Method Using a Custom-Tailored FP-Growth Implementation

The SPADE (spatio-temporal Spike PAttern Detection and Evaluation) method was developed to find reoccurring spatio-temporal patterns in neuronal spike activity (parallel spike trains). However, depending on the number of spike trains and the length of recording, this method can exhibit long runtimes. Based on a realistic benchmark data set, we identified that the combination of pattern mining (using the FP-Growth algorithm) and the result filtering account for 85–90% of the method's total runtime. Therefore, in this paper, we propose a customized FP-Growth implementation tailored to the requirements of SPADE, which significantly accelerates pattern mining and result filtering. Our version allows for parallel and distributed execution, and due to the improvements made, an execution on heterogeneous and low-power embedded devices is now also possible. The implementation has been evaluated using a traditional workstation based on an Intel Broadwell Xeon E5-1650 v4 as a baseline. Furthermore, the heterogeneous microserver platform RECS|Box has been used for evaluating the implementation on two HiSilicon Hi1616 (Kunpeng 916), an Intel Coffee Lake-ER Xeon E-2276ME, an Intel Broadwell Xeon D-D1577, and three NVIDIA Tegra devices (Jetson AGX Xavier, Jetson Xavier NX, and Jetson TX2). Depending on the platform, our implementation is between 27 and 200 times faster than the original implementation. At the same time, the energy consumption was reduced by up to two orders of magnitude.

A Model of the Early Visual System Based on Parallel Spike-Sequence Detection, Showing Orientation Selectivity

Simple Summary

A computational model of primates’ early visual processing, showing orientation selectivity, is presented. The system importantly integrates two key elements: (1) a neuromorphic spike-decoding structure that considerably resembles the circuitry between layers IV and II/III of the primary visual cortex, both in topology and operation; (2) the plasticity of intrinsic excitability, to embed recent findings about the operation of the same area. The model is proposed as a tool for the analysis and reproduction of the orientation selectivity phenomenon, whose underlying neuronal-level computational mechanisms are today the subject of intense scrutiny. In response to rotated Gabor patches the model is able to exhibit realistic orientation tuning curves and to reproduce responses similar to those found in neurophysiological recordings from the primary visual cortex obtained under the same task, considering different stages of the network. This demonstrates its aptness to capture the mechanisms underlying the evoked response in the primary visual cortex. Our tool is available online, and can be expanded to other experiments using a dedicated software library developed by the authors, to elucidate the computational mechanisms underlying orientation selectivity.

Abstract

Since the first half of the twentieth century, numerous studies have been conducted on how the visual cortex encodes basic image features. One of the hallmarks of basic feature extraction is the phenomenon of orientation selectivity, of which the underlying neuronal-level computational mechanisms remain partially unclear despite being intensively investigated. In this work we present a reduced visual system model (RVSM) of the first level of scene analysis, involving the retina, the lateral geniculate nucleus and the primary visual cortex (V1), showing orientation selectivity. The detection core of the RVSM is the neuromorphic spike-decoding structure MNSD, which is able to learn and recognize parallel spike sequences and considerably resembles the neuronal microcircuits of V1 in both topology and operation. This structure is equipped with plasticity of intrinsic excitability to embed recent findings about V1 operation. The RVSM, which embeds 81 groups of MNSD arranged in 4 oriented columns, is tested using sets of rotated Gabor patches as input. Finally, synthetic visual evoked activity generated by the RVSM is compared with real neurophysiological signal from V1 area: (1) postsynaptic activity of human subjects obtained by magnetoencephalography and (2) spiking activity of macaques obtained by multi-tetrode arrays. The system is implemented using the NEST simulator. The results attest to a good level of resemblance between the model response and real neurophysiological recordings. As the RVSM is available online, and the model parameters can be customized by the user, we propose it as a tool to elucidate the computational mechanisms underlying orientation selectivity.

Formalizing planning and information search in naturalistic decision-making

Decisions made by mammals and birds are often temporally extended. They require planning and sampling of decision-relevant information. Our understanding of such decision-making remains in its infancy compared with simpler, forced-choice paradigms. However, recent advances in algorithms supporting planning and information search provide a lens through which we can explain neural and behavioral data in these tasks. We review these advances to obtain a clearer understanding for why planning and curiosity originated in certain species but not others; how activity in the medial temporal lobe, prefrontal and cingulate cortices may support these behaviors; and how planning and information search may complement each other as means to improve future action selection.

Scalable and accurate method for neuronal ensemble detection in spiking neural networks

We propose a novel, scalable, and accurate method for detecting neuronal ensembles from a population of spiking neurons. Our approach offers a simple yet powerful tool to study ensemble activity. It relies on clustering synchronous population activity (population vectors), allows the participation of neurons in different ensembles, has few parameters to tune and is computationally efficient. To validate the performance and generality of our method, we generated synthetic data, where we found that our method accurately detects neuronal ensembles for a wide range of simulation parameters. We found that our method outperforms current alternative methodologies. We used spike trains of retinal ganglion cells obtained from multi-electrode array recordings under a simple ON-OFF light stimulus to test our method. We found a consistent stimuli-evoked ensemble activity intermingled with spontaneously active ensembles and irregular activity. Our results suggest that the early visual system activity could be organized in distinguishable functional ensembles. We provide a Graphic User Interface, which facilitates the use of our method by the scientific community.

A novel retinal ganglion cell quantification tool based on deep learning

Glaucoma is a disease associated with the loss of retinal ganglion cells (RGCs), and remains one of the primary causes of blindness worldwide. Major research efforts are presently directed towards the understanding of disease pathogenesis and the development of new therapies, with the help of rodent models as an important preclinical research tool. The ultimate goal is reaching neuroprotection of the RGCs, which requires a tool to reliably quantify RGC survival. Hence, we demonstrate a novel deep learning pipeline that enables fully automated RGC quantification in the entire murine retina. This software, called RGCode (Retinal Ganglion Cell quantification based On DEep learning), provides a user-friendly interface that requires the input of RBPMS-immunostained flatmounts and returns the total RGC count, retinal area and density, together with output images showing the computed counts and isodensity maps. The counting model was trained on RBPMS-stained healthy and glaucomatous retinas, obtained from mice subjected to microbead-induced ocular hypertension and optic nerve crush injury paradigms. RGCode demonstrates excellent performance in RGC quantification as compared to manual counts. Furthermore, we convincingly show that RGCode has potential for wider application, by retraining the model with a minimal set of training data to count FluoroGold-traced RGCs.

A Minimally Invasive Experimental Model of Acute Ocular Hypertension with Acute Angle Closure Characteristics

Purpose

To describe a minimally invasive experimental model of acute ocular hypertension (OHT) with characteristics of acute angle closure (AAC).

Methods

Adult C57/Bl6 mice (n = 31) were subjected to OHT in one eye using a modified circumlimbal suture technique that elevated intraocular pressure (IOP) for 30 minutes. Contralateral un-operated eyes served as controls. IOP, anterior segment optical coherence tomography, and fundus fluorescein angiography (FFA) were performed. The positive scotopic threshold response (pSTR) and a-wave and b-wave amplitudes were also evaluated. Retinal tissues were immunostained for the retinal ganglion cell (RGC) marker RBPMS and the glial marker GFAP.

Results

OHT eyes developed shallower anterior chambers and dilated pupils. FFA showed focal leakage in 32.2% of OHT eyes, but in none of the control eyes. pSTR was significantly reduced at week 1 in OHT eyes compared to control eyes (57.3 ± 7.2 µV vs. 106.9 ± 24.8 µV; P < 0.05), but a- and b-waves were unaffected. GFAP was upregulated in OHT eyes but not in control eyes or eyes that had been sutured without OHT. RGC density was reduced in OHT eyes after 4 weeks (3857 ± 143.8) vs. control eyes (4469 ± 176.0) (P < 0.05).

Conclusions

Our minimally invasive model resulted in acute OHT with characteristics of AAC in the absence of non-OHT-related neuroinflammatory changes arising from ocular injury alone.

Translational Relevance

This model provides a valuable approach to studying specific characteristics of a severe blinding disease in an experimental setting. Focal areas of ischemia were demonstrated, consistent with clinical studies of acute angle closure patients elsewhere, which may indicate the need for further research into how this could affect visual outcome in these patients.

Longitudinal outcomes of circumlimbal suture model-induced chronic ocular hypertension in Sprague-Dawley albino rats

PURPOSE

To characterise longitudinal structural and functional changes in albino Sprague-Dawley rats following circumlimbal suture ocular hypertension (OHT) induction.

METHODS

Ten-week-old rats (n = 24) underwent suture implantation around the limbal region in both eyes. On the next day, the suture was removed from one eye (control eyes) and left intact in the other eye (OHT eyes) of each animal. Intraocular pressure (IOP) was monitored weekly twice for the next 15 weeks. Optical coherence tomography (OCT) and electroretinogram (ERG) were measured at baseline and weeks 4, 8, 12, and 15, and eyes were then collected for histological assessment.

RESULTS

Sutured eyes (n = 12) developed IOP elevation of ~ 50% in the first 2 weeks that was sustained at ~ 25% above the control eye up to week 15 (p = 0.001). Animals with insufficient IOP elevation (n = 6), corneal changes (n = 3), and attrition (n = 3) were excluded from the analysis. OHT eyes developed significant retinal nerve fibre layer (RNFL) thinning (week 4: - 19 ± 14%, p = 0.10; week 8: - 17 ± 12%, p = 0.04; week 12: - 16 ± 10%, p = 0.04, relative to baseline) and reduction in retinal ganglion cell (RGC) density (- 32 ± 26%, p = 0.02). At week 15, both inner (9 ± 7%, p = 0.01) and outer retinal layer thicknesses (6.0 ± 5%, p = 0.001) showed a mild increase in thicknesses. The positive scotopic threshold response (- 28 ± 25%, p = 0.04) and a-wave were significantly reduced at week 12 (- 35 ± 21%; p = 0.04), whereas b-wave was not significantly affected (week 12: - 18 ± 27%, p = 0.24).

CONCLUSION

The circumlimbal suture model produced a chronic, moderate IOP elevation in an albino strain that led to RNFL thinning and reduced RGC density along with the reductions in ganglion and photoreceptoral cell functions. There was a small thickening in both outer and inner retinal layers.

Delirium screening in the intensive care unit using emerging QEEG techniques: A pilot study

Delirium is an under-diagnosed yet frequently occurring clinical complication with potentially serious consequences for intensive care unit (ICU) patients. Diagnosis is currently reactive and based upon qualitative assessment of the patient's cognitive status by ICU staff. Here, we conducted a preliminary investigation into whether emerging quantitative electroencephalography (QEEG) analysis techniques can accurately discriminate between delirious and non-delirious patients in an ICU setting. Resting EEG recordings from 5 ICU patients in a state of delirium and 5 age matched control patients were analyzed using autoregressive spectral estimation for quantification of EEG power and renormalized partial directed coherence for analysis of directed functional connectivity. Delirious subjects exhibited pronounced EEG slowing as well as severe general loss of directed functional connectivity between recording sites. Distinction between groups based on these parameters was surprisingly clear given the low sample size employed. Furthermore, by targeting the electrode positions where effects were most apparent it was possible to clearly segregate patients using only 3 scalp electrodes. These findings indicate that quantitative diagnosis and monitoring of delirium is not only possible using emerging QEEG methods but is also accomplishable using very low-density electrode systems.

Potential mechanisms of retinal ganglion cell type-specific vulnerability in glaucoma

Glaucoma is a neurodegenerative disease characterised by progressive damage to the retinal ganglion cells (RGCs), the output neurons of the retina. RGCs are a heterogenous class of retinal neurons which can be classified into multiple types based on morphological, functional and genetic characteristics. This review examines the body of evidence supporting type-specific vulnerability of RGCs in glaucoma and explores potential mechanisms by which this might come about. Studies of donor tissue from glaucoma patients have generally noted greater vulnerability of larger RGC types. Models of glaucoma induced in primates, cats and mice also show selective effects on RGC types - particularly OFF RGCs. Several mechanisms may contribute to type-specific vulnerability, including differences in the expression of calcium-permeable receptors (for example pannexin-1, P2X7, AMPA and transient receptor potential vanilloid receptors), the relative proximity of RGCs and their dendrites to blood supply in the inner plexiform layer, as well as differing metabolic requirements of RGC types. Such differences may make certain RGCs more sensitive to intraocular pressure elevation and its associated biomechanical and vascular stress. A greater understanding of selective RGC vulnerability and its underlying causes will likely reveal a rich area of investigation for potential treatment targets.

3d-SPADE: Significance evaluation of spatio-temporal patterns of various temporal extents

The Spike Pattern Detection and Evaluation (SPADE) analysis is a method to find reoccurring spike patterns in parallel spike train data, and to determine their statistical significance. Here we introduce an extension of the original statistical testing procedure which explicitly accounts for the temporal duration of the patterns. The extension improves the performance in the presence of patterns with different durations, as here demonstrated by application to various synthetic data. We further introduce an implementation of SPADE in form of a sub-module of the Python library Elephant (ELEctroPHysiological ANalysis Toolkit). The code is made publicly available on GitHub, together with detailed documentation, tutorials, and the results presented here.

Comparing Open-Source Toolboxes for Processing and Analysis of Spike and Local Field Potentials Data

Analysis of spike and local field potential (LFP) data is an essential part of neuroscientific research. Today there exist many open-source toolboxes for spike and LFP data analysis implementing various functionality. Here we aim to provide a practical guidance for neuroscientists in the choice of an open-source toolbox best satisfying their needs. We overview major open-source toolboxes for spike and LFP data analysis as well as toolboxes with tools for connectivity analysis, dimensionality reduction and generalized linear modeling. We focus on comparing toolboxes functionality, statistical and visualization tools, documentation and support quality. To give a better insight, we compare and illustrate functionality of the toolboxes on open-access dataset or simulated data and make corresponding MATLAB scripts publicly available.

Unsupervised Detection of Cell-Assembly Sequences by Similarity-Based Clustering

Neurons which fire in a fixed temporal pattern (i.e., "cell assemblies") are hypothesized to be a fundamental unit of neural information processing. Several methods are available for the detection of cell assemblies without a time structure. However, the systematic detection of cell assemblies with time structure has been challenging, especially in large datasets, due to the lack of efficient methods for handling the time structure. Here, we show a method to detect a variety of cell-assembly activity patterns, recurring in noisy neural population activities at multiple timescales. The key innovation is the use of a computer science method to comparing strings ("edit similarity"), to group spikes into assemblies. We validated the method using artificial data and experimental data, which were previously recorded from the hippocampus of male Long-Evans rats and the prefrontal cortex of male Brown Norway/Fisher hybrid rats. From the hippocampus, we could simultaneously extract place-cell sequences occurring on different timescales during navigation and awake replay. From the prefrontal cortex, we could discover multiple spike sequences of neurons encoding different segments of a goal-directed task. Unlike conventional event-driven statistical approaches, our method detects cell assemblies without creating event-locked averages. Thus, the method offers a novel analytical tool for deciphering the neural code during arbitrary behavioral and mental processes.

Structural Asymmetry in the Frontal and Temporal Lobes Is Associated with PCSK6 VNTR Polymorphism

The nodal cascade influences the development of bodily asymmetries in humans and other vertebrates. The gene PCSK6 has shown a regulatory function during left-right axis formation and is therefore thought to influence bodily left-right asymmetries. However, it is not clear if variation in this gene is also associated with structural asymmetries in the brain. We genotyped an intronic 33bp PCSK6 variable number tandem repeat (VNTR) polymorphism that has been associated with handedness in a cohort of healthy adults. We acquired T1-weighted structural MRI images of 320 participants and defined cortical surface and thickness for each HCP region. The results demonstrate a significant association between PCSK6 VNTR genotypes and gray matter asymmetry in the superior temporal sulcus, which is involved in voice perception. Heterozygous individuals who carry a short (≤ 6 repeats) and a long (≥ 9 repeats) PCSK6 VNTR allele show stronger rightward asymmetry. Further associations were evident in the dorsolateral prefrontal cortex. Here, individuals homozygous for short alleles show a more pronounced asymmetry. This shows that PCSK6, a gene that has been implicated in the ontogenesis of bodily asymmetries by regulating the nodal cascade, is also relevant for structural asymmetries in the human brain.

Reproducible Neural Network Simulations: Statistical Methods for Model Validation on the Level of Network Activity Data

Computational neuroscience relies on simulations of neural network models to bridge the gap between the theory of neural networks and the experimentally observed activity dynamics in the brain. The rigorous validation of simulation results against reference data is thus an indispensable part of any simulation workflow. Moreover, the availability of different simulation environments and levels of model description require also validation of model implementations against each other to evaluate their equivalence. Despite rapid advances in the formalized description of models, data, and analysis workflows, there is no accepted consensus regarding the terminology and practical implementation of validation workflows in the context of neural simulations. This situation prevents the generic, unbiased comparison between published models, which is a key element of enhancing reproducibility of computational research in neuroscience. In this study, we argue for the establishment of standardized statistical test metrics that enable the quantitative validation of network models on the level of the population dynamics. Despite the importance of validating the elementary components of a simulation, such as single cell dynamics, building networks from validated building blocks does not entail the validity of the simulation on the network scale. Therefore, we introduce a corresponding set of validation tests and present an example workflow that practically demonstrates the iterative model validation of a spiking neural network model against its reproduction on the SpiNNaker neuromorphic hardware system. We formally implement the workflow using a generic Python library that we introduce for validation tests on neural network activity data. Together with the companion study (Trensch et al., 2018), the work presents a consistent definition, formalization, and implementation of the verification and validation process for neural network simulations.

Detecting neural assemblies in calcium imaging data

BACKGROUND

Activity in populations of neurons often takes the form of assemblies, where specific groups of neurons tend to activate at the same time. However, in calcium imaging data, reliably identifying these assemblies is a challenging problem, and the relative performance of different assembly-detection algorithms is unknown.

RESULTS

To test the performance of several recently proposed assembly-detection algorithms, we first generated large surrogate datasets of calcium imaging data with predefined assembly structures and characterised the ability of the algorithms to recover known assemblies. The algorithms we tested are based on independent component analysis (ICA), principal component analysis (Promax), similarity analysis (CORE), singular value decomposition (SVD), graph theory (SGC), and frequent item set mining (FIM-X). When applied to the simulated data and tested against parameters such as array size, number of assemblies, assembly size and overlap, and signal strength, the SGC and ICA algorithms and a modified form of the Promax algorithm performed well, while PCA-Promax and FIM-X did less well, for instance, showing a strong dependence on the size of the neural array. Notably, we identified additional analyses that can improve their importance. Next, we applied the same algorithms to a dataset of activity in the zebrafish optic tectum evoked by simple visual stimuli, and found that the SGC algorithm recovered assemblies closest to the averaged responses.

CONCLUSIONS

Our findings suggest that the neural assemblies recovered from calcium imaging data can vary considerably with the choice of algorithm, but that some algorithms reliably perform better than others. This suggests that previous results using these algorithms may need to be reevaluated in this light.

Reproducing Polychronization: A Guide to Maximizing the Reproducibility of Spiking Network Models

Any modeler who has attempted to reproduce a spiking neural network model from its description in a paper has discovered what a painful endeavor this is. Even when all parameters appear to have been specified, which is rare, typically the initial attempt to reproduce the network does not yield results that are recognizably akin to those in the original publication. Causes include inaccurately reported or hidden parameters (e.g., wrong unit or the existence of an initialization distribution), differences in implementation of model dynamics, and ambiguities in the text description of the network experiment. The very fact that adequate reproduction often cannot be achieved until a series of such causes have been tracked down and resolved is in itself disconcerting, as it reveals unreported model dependencies on specific implementation choices that either were not clear to the original authors, or that they chose not to disclose. In either case, such dependencies diminish the credibility of the model's claims about the behavior of the target system. To demonstrate these issues, we provide a worked example of reproducing a seminal study for which, unusually, source code was provided at time of publication. Despite this seemingly optimal starting position, reproducing the results was time consuming and frustrating. Further examination of the correctly reproduced model reveals that it is highly sensitive to implementation choices such as the realization of background noise, the integration timestep, and the thresholding parameter of the analysis algorithm. From this process, we derive a guideline of best practices that would substantially reduce the investment in reproducing neural network studies, whilst simultaneously increasing their scientific quality. We propose that this guideline can be used by authors and reviewers to assess and improve the reproducibility of future network models.

Methods for identification of spike patterns in massively parallel spike trains

Temporally, precise correlations between simultaneously recorded neurons have been interpreted as signatures of cell assemblies, i.e., groups of neurons that form processing units. Evidence for this hypothesis was found on the level of pairwise correlations in simultaneous recordings of few neurons. Increasing the number of simultaneously recorded neurons increases the chances to detect cell assembly activity due to the larger sample size. Recent technological advances have enabled the recording of 100 or more neurons in parallel. However, these massively parallel spike train data require novel statistical tools to be analyzed for correlations, because they raise considerable combinatorial and multiple testing issues. Recently, various of such methods have started to develop. First approaches were based on population or pairwise measures of synchronization, and later led to methods for the detection of various types of higher-order synchronization and of spatio-temporal patterns. The latest techniques combine data mining with analysis of statistical significance. Here, we give a comparative overview of these methods, of their assumptions and of the types of correlations they can detect.

PRANAS: A New Platform for Retinal Analysis and Simulation

The retina encodes visual scenes by trains of action potentials that are sent to the brain via the optic nerve. In this paper, we describe a new free access user-end software allowing to better understand this coding. It is called PRANAS (https://pranas.inria.fr), standing for Platform for Retinal ANalysis And Simulation. PRANAS targets neuroscientists and modelers by providing a unique set of retina-related tools. PRANAS integrates a retina simulator allowing large scale simulations while keeping a strong biological plausibility and a toolbox for the analysis of spike train population statistics. The statistical method (entropy maximization under constraints) takes into account both spatial and temporal correlations as constraints, allowing to analyze the effects of memory on statistics. PRANAS also integrates a tool computing and representing in 3D (time-space) receptive fields. All these tools are accessible through a friendly graphical user interface. The most CPU-costly of them have been implemented to run in parallel.